Let me explain the main options for running subflows in Prefect 3.x: 1. Direct Call Syntax (Most Common) This is the simplest and recommended way to run a subflow. You just call it like a regular Python function within your main flow:

python
from prefect import flow

@flow
def subflow(x: int):
    return x * 2

@flow
def main_flow():
    # Direct call syntax
    result = subflow(5)
    return result

2. Using with Parameters You can pass parameters to subflows just like regular functions:

python
from prefect import flow

@flow
def subflow(x: int, y: str):
    return f"{y}: {x}"

@flow
def main_flow(value: int):
    # Call with multiple parameters
    result = subflow(value, "Result")
    return result

The subflow will be automatically tracked as part of the parent flow's execution, and you'll see it in the Prefect UI as a nested flow run. Key Points to Remember: - Subflows run in the same process as the parent flow by default - They appear as nested flows in the UI, making it easier to track execution - They inherit the parent flow's context - They can have their own task runners, retries, and other flow-specific configurations Best Practices: 1. Use subflows to organize logical units of work 2. Use them to create reusable components that can be called from different parent flows 3. Consider using subflows when you want separate monitoring or retry logic for a specific part of your workflow Note: While there are more advanced patterns possible with runners and deployments, for most use cases, the direct call syntax is the recommended approach as it's simple and maintainable. If you have a specific use case in mind or need to explore more advanced patterns (like running subflows on different infrastructure), let me know and I can provide more targeted guidance.

You can list your deployments with:

bash
prefect deployment ls

Alternative approach: If you don't want to store the deployment ID in your code, you can also look up the deployment by name:

python
@flow
async def main_flow():
    async with get_client() as client:
        # Find the deployment by name
        deployments = await client.read_deployments(
            flows={"name": {"any_": ["extract_data"]}},  # Replace with your flow name
        )
        if not deployments:
            raise ValueError("Deployment not found")
        
        deployment = deployments[0]  # Use the first matching deployment
        
        # Create and wait for the flow run as before
        flow_run = await client.create_flow_run_from_deployment(
            deployment_id=deployment.id,
            parameters={"your": "parameters"},
        )
        # ... rest of the code as above

This approach is more flexible as it doesn't hardcode the deployment ID, but make sure your flow name is unique enough to find the correct deployment. Would you like me to explain any part of this in more detail or help you with a specific implementation?

Ah, thank you for the detailed clarification! This is a perfect example of distributed flow orchestration. Yes, I can confirm that your current approach (

await tools.extract_data(...)

) is executing the flow directly rather than triggering its deployment in Work Pool B. Let me provide a solution specifically for your distributed work pool setup:

python
from prefect import flow
from prefect.client import get_client
from prefect.flow_runs import wait_for_flow_run

@flow
async def main_flow():
    # This flow is deployed to Work Pool A
    
    async with get_client() as client:
        # Create a flow run that will be picked up by Work Pool B
        flow_run = await client.create_flow_run_from_deployment(
            deployment_id="extract-data-deployment-id",  # The deployment ID for extract_data
            parameters={
                "file_name": file  # Pass through the same parameters
            }
        )
        
        # Wait for the extract_data flow to complete and get its result
        flow_run_state = await wait_for_flow_run(flow_run.id)
        
        if flow_run_state.is_completed():
            # Get the result from the completed flow run
            is_table = flow_run_state.result()
            return is_table
        else:
            raise Exception(f"extract_data flow failed with state: {flow_run_state.type}")

This approach: 1. Properly triggers the

extract_data

deployment in Work Pool B 2. Maintains separation between the flows and their respective work pools 3. Still allows for orchestration and data passing between flows 4. Preserves the async nature of your flows A few important points: 1. Error Handling: You might want to add more robust error handling:

python
@flow
async def main_flow():
    async with get_client() as client:
        try:
            flow_run = await client.create_flow_run_from_deployment(
                deployment_id="extract-data-deployment-id",
                parameters={"file_name": file}
            )
            
            flow_run_state = await wait_for_flow_run(
                flow_run.id,
                timeout=1800  # Optional: set custom timeout (in seconds)
            )
            
            if flow_run_state.is_completed():
                return flow_run_state.result()
            else:
                raise Exception(
                    f"extract_data flow failed with state: {flow_run_state.type}"
                    f"\nFlow run ID: {flow_run.id}"
                )
        except Exception as e:
            # Handle any API or timeout errors
            raise Exception(f"Failed to execute extract_data: {str(e)}")

2. Finding the Deployment: If you don't want to hardcode the deployment ID, you can look it up by name:

python
deployments = await client.read_deployments(
    flows={"name": {"any_": ["extract_data"]}},
    work_pools={"name": {"any_": ["Work Pool B"]}}  # Ensure correct work pool
)
if not deployments:
    raise ValueError("extract_data deployment not found in Work Pool B")
deployment = deployments[0]

3. Monitoring: You can get the flow run URL for monitoring:

python
flow_run = await client.create_flow_run_from_deployment(...)
print(f"Started extract_data flow run: {flow_run.id}")
# If you have PREFECT_UI_URL set in your environment:
print(f"Monitor at: {flow_run.ui_url}")

This setup ensures that: -

main_flow

runs in Work Pool A - When it needs to run

extract_data

, it creates a new flow run via the deployment - That flow run is picked up by a worker in Work Pool B -

main_flow

waits for the result before continuing Would you like me to explain any part of this in more detail or help with implementation specifics?

Yes! There is a way to maintain the parent-child relationship in the UI while still executing the flow via its deployment. Here's how to modify your code:

python
from prefect import flow
from prefect.client import get_client
from prefect.flow_runs import wait_for_flow_run

@flow
async def main_flow():
    async with get_client() as client:
        # Get the current flow run ID
        current_flow_run = await client.get_current_flow_run()
        
        # Create flow run with parent relationship
        flow_run = await client.create_flow_run_from_deployment(
            deployment_id="extract-data-deployment-id",
            parameters={"file_name": file},
            parent_flow_run_id=current_flow_run.id  # This creates the parent-child link
        )
        
        flow_run_state = await wait_for_flow_run(flow_run.id)
        
        if flow_run_state.is_completed():
            return flow_run_state.result()
        else:
            raise Exception(f"extract_data flow failed with state: {flow_run_state.type}")

The key addition is the

parent_flow_run_id

parameter. By passing the current flow run's ID, you establish the parent-child relationship that will be reflected in the UI. For better error handling and clarity, here's a more complete version:

python
from prefect import flow, get_run_logger
from prefect.client import get_client
from prefect.flow_runs import wait_for_flow_run

@flow
async def main_flow():
    logger = get_run_logger()
    
    async with get_client() as client:
        try:
            # Get current flow run
            current_flow_run = await client.get_current_flow_run()
            
            <http://logger.info|logger.info>("Creating extract_data flow run...")
            
            # Create the child flow run
            flow_run = await client.create_flow_run_from_deployment(
                deployment_id="extract-data-deployment-id",
                parameters={"file_name": file},
                parent_flow_run_id=current_flow_run.id,
                name="extract-data-subflow"  # Optional: give it a descriptive name
            )
            
            <http://logger.info|logger.info>(
                f"Started extract_data as subflow. "
                f"Flow run ID: {flow_run.id}"
                f"\nMonitor at: {flow_run.ui_url}"
            )
            
            # Wait for completion
            flow_run_state = await wait_for_flow_run(
                flow_run.id,
                timeout=1800  # Optional: adjust timeout as needed
            )
            
            if flow_run_state.is_completed():
                <http://logger.info|logger.info>("Subflow completed successfully")
                return flow_run_state.result()
            else:
                raise Exception(
                    f"Subflow failed with state: {flow_run_state.type}"
                )
                
        except Exception as e:
            logger.error(f"Error executing subflow: {str(e)}")
            raise

This approach gives you: 1. Proper parent-child relationship visible in the UI 2. Execution in the correct work pool via deployment 3. Better observability with logging 4. Proper error handling and status tracking Alternative: Using

run_deployment

There's also another approach using the higher-level

run_deployment

function:

python
from prefect import flow
from prefect.deployments import run_deployment

@flow
async def main_flow():
    # This automatically maintains the parent-child relationship
    result = await run_deployment(
        name="extract-data/deployment-name",  # "flow-name/deployment-name"
        parameters={"file_name": file},
        timeout=1800,
        as_subflow=True  # This ensures it shows up as a subflow
    )
    return result

Both approaches will work, but: -

run_deployment

is slightly more concise and handles some details for you -

create_flow_run_from_deployment

gives you more fine-grained control Choose based on your needs:

- Use

run_deployment

if you want a simpler, more straightforward approach - Use

create_flow_run_from_deployment

if you need more control over the flow run creation and waiting process Would you like me to explain any of these approaches in more detail?

Based on the research, let's try a more robust approach that should help avoid the data integrity conflict. Here's the modified code:

python
from prefect import flow, get_run_logger
from prefect.client import get_client
from prefect.flow_runs import wait_for_flow_run
import uuid

@flow
async def main_flow():
    logger = get_run_logger()
    
    async with get_client() as client:
        try:
            # Generate a unique name for this run
            run_name = f"extract-data-{uuid.uuid4().hex[:8]}"
            
            # Create the flow run with unique identifiers
            flow_run = await client.create_flow_run_from_deployment(
                deployment_id="extract-data-deployment-id",
                parameters={"file_name": file},
                name=run_name,  # Ensure unique name
                tags=["triggered-from-main-flow"]  # Optional: helpful for tracking
            )
            
            <http://logger.info|logger.info>(f"Created flow run with ID: {flow_run.id}")
            
            flow_run_state = await wait_for_flow_run(flow_run.id)
            
            if flow_run_state.is_completed():
                return flow_run_state.result()
            else:
                raise Exception(f"Flow run failed with state: {flow_run_state.type}")
                
        except Exception as e:
            logger.error(f"Error creating or executing flow run: {str(e)}")
            raise

If you're still getting the error, let's try these troubleshooting steps: 1. First, verify your deployment ID is correct:

python
async with get_client() as client:
    # List all deployments to find the correct ID
    deployments = await client.read_deployments()
    for dep in deployments:
        print(f"Name: {dep.name}, ID: {dep.id}")

2. Alternatively, try using

run_deployment

which might handle some edge cases better:

python
from prefect import flow
from prefect.deployments import run_deployment

@flow
async def main_flow():
    try:
        result = await run_deployment(
            name="extract-data/your-deployment-name",  # Use "flow-name/deployment-name"
            parameters={"file_name": file},
            timeout=1800
        )
        return result
    except Exception as e:
        print(f"Error running deployment: {str(e)}")
        raise

3. If you're still having issues, let's check the versions:

python
from prefect import __version__
print(f"Prefect version: {__version__}")

Could you please: 1. Confirm which approach you're using (create_flow_run_from_deployment or run_deployment)? 2. Share your Prefect version? 3. Let me know if you see any specific error messages in the server logs? This will help me provide a more targeted solution for your specific setup.

Ah, I see the issue. This error occurs because the flow runs are executing in different work pools and potentially different environments, leading to path mismatches. Let's handle this by ensuring proper path handling between the flows. Here are a few approaches to solve this: 1. Use Absolute Paths or Environment Variables

python
import os
from prefect import flow, get_run_logger
from prefect.deployments import run_deployment

# Define a common base path via environment variable
# Set this in both work pool environments
STORAGE_PATH = os.getenv("STORAGE_PATH", "/path/to/shared/storage")

@flow
async def extract_data(file_name: str):
    # Use the environment-aware path
    result_path = os.path.join(STORAGE_PATH, file_name)
    # ... your extraction logic ...
    return result_path

@flow
async def main_flow():
    logger = get_run_logger()
    
    try:
        result = await run_deployment(
            name="extract-data/your-deployment-name",
            parameters={
                "file_name": file,
            },
            timeout=1800
        )
        
        # Translate the path if needed
        result_path = os.path.join(STORAGE_PATH, os.path.basename(result))
        return result_path
        
    except Exception as e:
        logger.error(f"Error: {str(e)}")
        raise

2. Use Prefect Artifacts for Result Sharing

python
from prefect import flow, get_run_logger
from prefect.artifacts import create_markdown_artifact
import json

@flow
async def extract_data(file_name: str):
    # ... your extraction logic ...
    
    # Store the result path as an artifact
    result_info = {
        "file_path": result_path,
        "status": "completed"
    }
    await create_markdown_artifact(
        key="extract-data-result",
        value=json.dumps(result_info),
        description="Extract data result path"
    )
    return result_info

@flow
async def main_flow():
    logger = get_run_logger()
    
    try:
        result = await run_deployment(
            name="extract-data/your-deployment-name",
            parameters={"file_name": file},
            timeout=1800
        )
        
        # Result will contain the info we need
        if isinstance(result, dict) and "file_path" in result:
            # Handle the path appropriately for your environment
            return result["file_path"]
            
    except Exception as e:
        logger.error(f"Error: {str(e)}")
        raise

3. Use a Shared Storage Solution

python
from prefect import flow
from prefect.deployments import run_deployment

# Configure this in both environments to point to the same storage
SHARED_STORAGE_BASE = "/mnt/shared" # or "<s3://your-bucket>" etc.

@flow
async def extract_data(file_name: str):
    # Use shared storage path
    result_path = f"{SHARED_STORAGE_BASE}/results/{file_name}"
    # ... your extraction logic ...
    return result_path

@flow
async def main_flow():
    result_path = await run_deployment(
        name="extract-data/your-deployment-name",
        parameters={"file_name": file},
        timeout=1800
    )
    # The path will be valid in both environments
    return result_path

Best Practices for Path Handling: 1. Environment Configuration:

bash
# In both work pool environments:
export STORAGE_PATH=/path/to/shared/storage

2. Use Path Utilities:

python
from pathlib import Path

@flow
async def handle_paths(file_path: str):
    # Convert to Path object for better handling
    path = Path(file_path)
    if not path.is_absolute():
        path = Path(os.getenv("STORAGE_PATH")) / path
    return str(path)

3. Consider Using Remote Storage: - S3, GCS, or Azure Blob Storage can be accessed consistently across different environments - Network file systems (NFS) if both work pools are in the same network Choose the approach that best fits your infrastructure: - If you have shared storage accessible from both work pools, use approach #3

- If you need to handle different environments, use approach #1 with environment variables - If you want to avoid path issues entirely, use approach #2 with artifacts Would you like me to elaborate on any of these approaches or help you implement one specific to your setup?